Surrogate-Assisted Modeling and Robust Optimization of a Micro Gas Turbine Plant With Carbon Capture

2019 ◽  
Author(s):  
Simone Giorgetti ◽  
Diederik Coppitters ◽  
Francesco Contino ◽  
Ward De Paepe ◽  
Laurent Bricteux ◽  
...  
Keyword(s):  
Robust Optimization ◽  
Gas Turbines ◽  
Carbon Capture ◽  
Computational Cost ◽  
Gaussian Process Regression ◽  
Aspen Plus ◽  
Optimization Approach ◽  
Co2 Absorption ◽  
Exhaust Gas ◽  
Nsga Ii

Abstract The growing share of wind and solar power in the total energy mix has caused severe problems in balancing the electrical power production. Consequently, in the future, all fossil fuel-based electricity generation will need to be run on a completely flexible basis. Micro Gas Turbines (mGTs) constitutes a mature technology which can offer such flexibility. Even though their greenhouse gas emissions are already very low, stringent carbon reduction targets will require them to be completely carbon neutral: this constraint implies the adoption of post-combustion Carbon Capture (CC) on these energy systems. To reduce the CC energy penalty, Exhaust Gas Recirculation (EGR) can be applied to the mGTs increasing the CO2 content in the exhaust gas and reducing the mass flow rate of flue gas to be treated. As a result, a lower investment and operational cost of the CC unit can be achieved. In spite of this attractive solution, an in-depth study along with a robust optimization of this system has not yet been carried out. Hence, in this paper, a typical mGT with EGR has been coupled with an amine-based CC plant and simulated using the software Aspen Plus®. A rigorous rate-based simulation of the CO2 absorption and desorption in the CC unit offers an accurate prediction; however, its time complexity and convergence difficulty are severe limitations for a stochastic optimization. Therefore, a surrogate-based optimization approach has been used, which makes use of a Gaussian Process Regression (GPR) model, trained using the Aspen Plus® data, to quickly find operating points of the plant at a very low computational cost. Using the validated surrogate model, a robust optimization using a Non-dominated Sorting Genetic Algorithm II (NSGA II) has been carried out, assessing the influence of each input uncertainty and varying several design variables. As a general result, the analysed power plant proves to be intrinsically very robust, even when the input variables are affected by strong uncertainties.

Surrogate-Assisted Modeling and Robust Optimization of a Micro Gas Turbine Plant With Carbon Capture

2019 ◽  
Vol 142 (1) ◽  
Author(s):  
Simone Giorgetti ◽  
Diederik Coppitters ◽  
Francesco Contino ◽  
Ward De Paepe ◽  
Laurent Bricteux ◽  
...  
Keyword(s):  
Robust Optimization ◽  
Stochastic Optimization ◽  
Carbon Capture ◽  
Electrical Power ◽  
Computational Cost ◽  
Gaussian Process Regression ◽  
Optimization Approach ◽  
Co2 Absorption ◽  
Carbon Reduction ◽  
Input Variables

Abstract The growing share of wind and solar power in the total energy mix has caused severe problems in balancing the electrical power production. Consequently, in the future, all fossil fuel-based electricity generation will need to be run on a completely flexible basis. Microgas turbines (mGTs) constitute a mature technology which can offer such flexibility. Even though their greenhouse gas emissions are already very low, stringent carbon reduction targets will require them to be completely carbon neutral: this constraint implies the adoption of postcombustion carbon capture (CC) on these energy systems. Despite this attractive solution, an in-depth study along with a robust optimization of this system has not yet been carried out. Hence, in this paper, a typical mGT with exhaust gas recirculation has been coupled with an amine-based CC plant and simulated using the software aspenplus. A rigorous rate-based simulation of the CO2 absorption and desorption in the CC unit offers an accurate prediction; however, its time complexity and convergence difficulty are severe limitations for a stochastic optimization. Therefore, a surrogate-based optimization approach has been used, which makes use of a Gaussian process regression (GPR) model, trained using the aspenplus data, to quickly find operating points of the plant at a very low computational cost. Using the validated surrogate model, a stochastic optimization has been carried out. As a general result, the analyzed power plant proves to be intrinsically very robust, even when the input variables are affected by strong uncertainties.

Feasibility Robust Optimization via Scenario Generation and Reduction

2018 ◽  
Author(s):  
Eliot Rudnick-Cohen ◽  
Jeffrey W. Herrmann ◽  
Shapour Azarm
Keyword(s):  
Robust Optimization ◽  
Optimization Problems ◽  
Computational Cost ◽  
Optimization Techniques ◽  
Scenario Generation ◽  
Test Problems ◽  
Optimization Approach ◽  
Uncertain Parameters ◽  
Worst Case ◽  
Robust Solution

Feasibility robust optimization techniques solve optimization problems with uncertain parameters that appear only in their constraint functions. Solving such problems requires finding an optimal solution that is feasible for all realizations of the uncertain parameters. This paper presents a new feasibility robust optimization approach involving uncertain parameters defined on continuous domains without any known probability distributions. The proposed approach integrates a new sampling-based scenario generation scheme with a new scenario reduction approach in order to solve feasibility robust optimization problems. An analysis of the computational cost of the proposed approach was performed to provide worst case bounds on its computational cost. The new proposed approach was applied to three test problems and compared against other scenario-based robust optimization approaches. A test was conducted on one of the test problems to demonstrate that the computational cost of the proposed approach does not significantly increase as additional uncertain parameters are introduced. The results show that the proposed approach converges to a robust solution faster than conventional robust optimization approaches that discretize the uncertain parameters.

Study of Using Exhaust Gas Recirculation on a Gas Turbine for Carbon Capture

2020 ◽  
Author(s):  
Dan Burnes ◽  
Priyank Saxena ◽  
Paul Dunn
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Carbon Capture ◽  
Hydrocarbon Fuel ◽  
Exhaust Gas Recirculation ◽  
Exhaust Gas ◽  
Hybrid Solutions ◽  
Industrial Gas Turbines ◽  
Industrial Gas Turbine ◽  
Gas Recirculation

Abstract The growing call of minimizing carbon dioxide and other greenhouse gases emitting from energy and transportation products will spur innovation to meet new stringent requirements while striving to preserve significant investments in the current infrastructure. This paper presents quantitative analysis of exhaust gas recirculation (EGR) on industrial gas turbines to enable carbon sequestration venturing towards emission free operation. This study will show the effect of using EGR on gas turbine performance and operation, combustion characteristics, and demonstrate potential hybrid solutions with detailed constituent accounting. Both single shaft and two shaft gas turbines for power generation and mechanically driven equipment are considered for application of this technology. One key element is assessing the combustion system operating at reduced O2 levels within the industrial gas turbine. With the gas turbine behavior operating with EGR defined at a reasonable operating state, a parametric study shows rates of CO2 sequestration along with quantifying supplemental O2 required at the inlet, if needed, to sustain combustion. With rates of capture known, a further exploration is examined reviewing potential utilities, monetizing these sequestered constituents. Ultimately, the objective is to preview a potential future of operating industrial gas turbines in a non-emissive and in some cases carbon negative manner while still using hydrocarbon fuel.

scholarly journals Non-Convex Feasibility Robust Optimization Via Scenario Generation and Local Refinement

2019 ◽  
Vol 142 (5) ◽  
Author(s):  
Eliot Rudnick-Cohen ◽  
Jeffrey W. Herrmann ◽  
Shapour Azarm
Keyword(s):  
Robust Optimization ◽  
Optimization Problems ◽  
Computational Cost ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Scenario Generation ◽  
Test Problems ◽  
Optimization Approach ◽  
Uncertain Parameters ◽  
Worst Case

Abstract Feasibility robust optimization techniques solve optimization problems with uncertain parameters that appear only in their constraint functions. Solving such problems requires finding an optimal solution that is feasible for all realizations of the uncertain parameters. This paper presents a new feasibility robust optimization approach involving uncertain parameters defined on continuous domains. The proposed approach is based on an integration of two techniques: (i) a sampling-based scenario generation scheme and (ii) a local robust optimization approach. An analysis of the computational cost of this integrated approach is performed to provide worst-case bounds on its computational cost. The proposed approach is applied to several non-convex engineering test problems and compared against two existing robust optimization approaches. The results show that the proposed approach can efficiently find a robust optimal solution across the test problems, even when existing methods for non-convex robust optimization are unable to find a robust optimal solution. A scalable test problem is solved by the approach, demonstrating that its computational cost scales with problem size as predicted by an analysis of the worst-case computational cost bounds.

Model-Based Thermodynamic Analysis of a Hydrogen-Fired Gas Turbine with External Exhaust Gas Recirculation

2021 ◽  
Author(s):  
Thomas Bexten ◽  
Sophia Jörg ◽  
Nils Petersen ◽  
Manfred Wirsum ◽  
Pei Liu ◽  
...  
Keyword(s):  
Power Generation ◽  
Natural Gas ◽  
Gas Turbine ◽  
Thermal Efficiency ◽  
Gas Turbines ◽  
Carbon Capture ◽  
Exhaust Gas ◽  
Model Based ◽  
Renewable Power ◽  
Renewable Power Generation

Abstract Climate science shows that the limitation of global warming requires a rapid transition towards net-zero emissions of greenhouse gases (GHG) on a global scale. Expanding renewable power generation is seen as an imperative measure within this transition. To compensate for the inherent volatility of renewable power generation, flexible and dispatchable power generation technologies such as gas turbines are required. If operated with CO2-neutral hydrogen or in combination with carbon capture plants, a GHG-neutral gas turbine operation could be achieved. An effective leverage to enhance carbon capture efficiency and a possible measure to safely burn hydrogen in gas turbines is the partial external recirculation of exhaust gas. By means of a model-based analysis of a gas turbine, the present study initially assesses the thermodynamic impact caused by a fuel switch from natural gas to hydrogen. Although positive trends such as increasing net electrical power output and thermal efficiency can be observed, the overall effect on the gas turbine process is only minor. In a following step, the partial external recirculation of exhaust gas is evaluated and compared both for the combustion of natural gas and hydrogen, regardless of potential combustor design challenges. The influence of altering working fluid properties throughout the whole gas turbine process is thermodynamically evaluated for ambient temperature recirculation and recirculation at an elevated temperature. A reduction in thermal efficiency can be observed as well as non-negligible changes of relevant process variables. These changes are more distinctive at a higher recirculation temperature

Performance Assessment of a Micro Gas Turbine Cycle With Exhaust Gas Recirculation Fueled by Biogas for Post-Combustion Carbon Capture Application

2018 ◽  
Author(s):  
Homam Nikpey Somehsaraei ◽  
Mohammad Mansouri Majoumerd ◽  
Mohsen Assadi
Keyword(s):  
Renewable Energy ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Carbon Capture ◽  
Exhaust Gas Recirculation ◽  
Exhaust Gas ◽  
Micro Gas Turbine ◽  
Wide Range ◽  
Gas Recirculation ◽  
Gas Turbine Cycle

As a renewable energy source, biogas produced from anaerobic digestion seems to play an important role in the energy market. Unlike wind and solar, which are intermittent, gas turbines fueled by biogas provide dispatchable renewable energy that can be ramped up and down to match the demand. If post-combustion carbon capture systems are implemented, they can also result in negative CO2 emissions. However, one of the major challenges here is the energy needed for CO2 chemical absorption in post-combustion capture, which is closely related to the concentration of CO2 in the exhaust gas upstream of the capture unit. This paper presents an evaluation of the effects of biogas and exhaust gas recirculation use on the performance of the gas turbine cycle for post-combustion CO2 capture application. The study is based on a combined heat and power micro gas turbine, Turbec T100, delivering 100kWe. The thermodynamic model of the gas turbine has been validated against experimental data obtained from test facilities in Norway and the United Kingdom. Based on the validated model, performance calculations for the baseline micro gas turbine (fueled by natural gas), biogas-fired cases and the cycle with exhaust gas recirculation have been carried out at various operational conditions and compared together. A wide range of biogas composition with varying methane content was assumed for this study. Necessary minor modifications to fuel valves and compressor were assumed to allow the engine operation with different biogas composition. The methodology and results are fully discussed in this paper.

scholarly journals How to compare performance of robust design optimization algorithms, including a novel method

2017 ◽  
Vol 31 (3) ◽  
pp. 286-297 ◽  
Author(s):  
Johan A. Persson ◽  
Johan Ölvander
Keyword(s):  
Robust Optimization ◽  
Design Optimization ◽  
Robust Design ◽  
Performance Metrics ◽  
Computational Cost ◽  
Optimization Method ◽  
Engineering Problem ◽  
Robust Design Optimization ◽  
Optimization Approach ◽  
Novel Method

AbstractThis paper proposes a method to compare the performances of different methods for robust design optimization of computationally demanding models. Its intended usage is to help the engineer to choose the optimization approach when faced with a robust optimization problem. This paper demonstrates the usage of the method to find the most appropriate robust design optimization method to solve an engineering problem. Five robust design optimization methods, including a novel method, are compared in the demonstration of the comparison method. Four of the five compared methods involve surrogate models to reduce the computational cost of performing robust design optimization. The five methods are used to optimize several mathematical functions that should be similar to the engineering problem. The methods are then used to optimize the engineering problem to confirm that the most suitable optimization method was identified. The performance metrics used are the mean value and standard deviation of the robust optimum as well as an index that combines the required number of simulations of the original model with the accuracy of the obtained solution. These measures represent the accuracy, robustness, and efficiency of the compared methods. The results of the comparison show that sequential robust optimization is the method with the best balance between accuracy and number of function evaluations. This is confirmed by the optimizations of the engineering problem. The comparison also shows that the novel method is better than its predecessor is.

An on-line Kriging metamodel assisted robust optimization approach under interval uncertainty

2017 ◽  
Vol 34 (2) ◽  
pp. 420-446 ◽  
Author(s):  
Qi Zhou ◽  
Ping Jiang ◽  
Xinyu Shao ◽  
Hui Zhou ◽  
Jiexiang Hu
Keyword(s):  
Robust Optimization ◽  
Optimization Problems ◽  
Computational Cost ◽  
Interval Uncertainty ◽  
Optimization Approach ◽  
Content Type ◽  
Computational Burden ◽  
Design Alternative ◽  
Kriging Metamodel ◽  
On Line

Purpose Uncertainty is inevitable in real-world engineering optimization. With an outer-inner optimization structure, most previous robust optimization (RO) approaches under interval uncertainty can become computationally intractable because the inner level must perform robust evaluation for each design alternative delivered from the outer level. This paper aims to propose an on-line Kriging metamodel-assisted variable adjustment robust optimization (OLK-VARO) to ease the computational burden of previous VARO approach. Design/methodology/approach In OLK-VARO, Kriging metamodels are constructed for replacing robust evaluations of the design alternative delivered from the outer level, reducing the nested optimization structure of previous VARO approach into a single loop optimization structure. An on-line updating mechanism is introduced in OLK-VARO to exploit the obtained data from previous iterations. Findings One nonlinear numerical example and two engineering cases have been used to demonstrate the applicability and efficiency of the proposed OLK-VARO approach. Results illustrate that OLK-VARO is able to obtain comparable robust optimums as to that obtained by previous VARO, while at the same time significantly reducing computational cost. Practical implications The proposed approach exhibits great capability for practical engineering design optimization problems under interval uncertainty. Originality/value The main contribution of this paper lies in the following: an OLK-VARO approach under interval uncertainty is proposed, which can significantly ease the computational burden of previous VARO approach.

Humidified Micro Gas Turbine for Carbon Capture Applications: Preliminary Experimental Results With CO2 Injection

2018 ◽  
Author(s):  
Simone Giorgetti ◽  
Alessandro Parente ◽  
Francesco Contino ◽  
Laurent Bricteux ◽  
Ward De Paepe
Keyword(s):  
Gas Turbines ◽  
Carbon Capture ◽  
Transition Period ◽  
Numerical Study ◽  
Co2 Concentration ◽  
Power Production ◽  
Experimental Results ◽  
Co2 Injection ◽  
Small Scale ◽  
Exhaust Gas

The large adoption of renewable energies is crucial to achieve a low-carbon economy, however, in the transition period, a flexible and clean production from fossil fuels is still necessary. With the current shift towards decentralized power production, micro Gas Turbines (mGTs) appear as a promising technology for small-scale generation. The target of a carbon-clean power production calls for the implementation of Carbon Capture Use and Storage (CCUS) technologies. Compared to coal fired power production, the low CO2 concentration in the exhaust gas of a mGT makes Carbon Capture (CC) much more expensive. However, the CO2 concentration can be increased by performing Exhaust Gas Recirculation (EGR), therefore reducing the CC energy penalty. Additionally, cycle humidification can also help to increase the electrical efficiency of the turbine plant. Nevertheless, the higher CO2 content in the inlet air, in combination with the high humidity level, will affect the operation of the mGT. This paper presents a numerical study of this innovative cycle combined with preliminary experimental validation of CO2 injection. To the authors’ best knowledge, experimental analysis of EGR together with humidification applied to a mGT has never been carried out. Experimental results showed a stable turbo-machinery operation under a moderate CO2 injection. The results of this paper are a first step towards a more severe dilution conditions, with the aim of a full implementation of EGR on a micro Humid Air Turbine (mHAT).

Model-Based Thermodynamic Analysis of a Hydrogen-Fired Gas Turbine With External Exhaust Gas Recirculation

2020 ◽  
Author(s):  
Thomas Bexten ◽  
Sophia Jörg ◽  
Nils Petersen ◽  
Manfred Wirsum ◽  
Pei Liu ◽  
...  
Keyword(s):  
Power Generation ◽  
Natural Gas ◽  
Gas Turbine ◽  
Thermal Efficiency ◽  
Gas Turbines ◽  
Carbon Capture ◽  
Electrical Power ◽  
Working Fluid ◽  
Exhaust Gas ◽  
Model Based

Abstract Climate science shows that the limitation of global warming requires a rapid transition towards net-zero emissions of green house gases (GHG) on a global scale. Expanding renewable power generation in a significant way is seen as an imperative measure within this transition. To compensate for the inherent volatility of wind- and solar-based power generation, flexible and dispatchable power generation technologies such as gas turbines are required. If operated with CO2-neutral fuels such as hydrogen or in combination with carbon capture plants, a GHG-neutral gas turbine operation could be achieved. An effective leverage to enhance carbon capture efficiency and a possible measure to safely burn hydrogen in gas turbines is the partial external recirculation of exhaust gas. By means of a model-based analysis of an industrial gas turbine, the present study initially assesses the thermodynamic impact caused by a fuel switch from natural gas to hydrogen. Although positive trends such as increasing net electrical power output and thermal efficiency can be observed, the overall effect on the gas turbine process is only minor. In a following step, the partial external recirculation of exhaust gas is evaluated and compared both for the combustion of natural gas and hydrogen, regardless of potential combustor design challenges. The influence of altering working fluid properties throughout the whole gas turbine process is thermodynamically evaluated for ambient temperature recirculation and recirculation at an elevated temperature. A reduction in thermal efficiency can be observed as well as non-negligible changes of relevant process variables. These changes are are more distinctive at a higher recirculation temperature.

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